Using learning algorithm behavior to chart task space: The DICES distance
Titel:
Using learning algorithm behavior to chart task space: The DICES distance
Auteur:
Peterson, Adam H. Martinez, Tony R.
Verschenen in:
Intelligent data analysis
Paginering:
Jaargang 14 (2010) nr. 3 pagina's 355-367
Jaar:
2010-05-20
Inhoud:
In theory, learning is not possible over all tasks in general. In practice, the tasks for which learning is desired exhibit significant regularity, which makes learning practical. For the most effective learning, it is valuable to understand the nature of this regularity and how it manifests in the tasks where learning is applied. This research presents the DICES distance metric for finding similarity between learning tasks. With this distance metric, a collection of learning tasks can be given a distance matrix. This distance matrix can be used for visualizing the relationships between learning tasks and searching through task space for tasks which are similar in nature. Examples of task visualization are given, and other possible applications of this tool are touched upon. Such applications include learning algorithm selection, transfer learning, and analysis of empirical results.
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